Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.541

Outlier detection

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Outlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 261–268 DOI: 10.1002/widm.19 This article is categorized under: Algorithmic Development > Scalable Statistical Methods Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Algorithmic Development > Statistics Technologies > Structure Discovery and Clustering

Box plot with outliers.

[ Normal View | Magnified View ]

Outliers in data of varying density.

[ Normal View | Magnified View ]

Scatter plot with an outlier (point O) that cannot be detected via univariate techniques.

[ Normal View | Magnified View ]

Browse by Topic

Technologies > Structure Discovery and Clustering
Algorithmic Development > Scalable Statistical Methods
Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Algorithmic Development > Statistics

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts